{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mPt5BHTwy_0F"
   },
   "source": [
    "# Preprocessing Data with Advanced Example using TensorFlow Transform\n",
    "\n",
    "## Learning objectives\n",
    "\n",
    "1. Create a tf.Transform `preprocessing_fn`.\n",
    "2. Transform the data.\n",
    "3. Create an input function for training.\n",
    "4. Build the model.\n",
    "5. Train and Evaluate the model.\n",
    "6. Export the model.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "***The Feature Engineering Component of TensorFlow Extended (TFX)***\n",
    "\n",
    "This notebook provides a somewhat more advanced example of how <a target='_blank' href='https://www.tensorflow.org/tfx/transform/'>TensorFlow Transform</a> (`tf.Transform`) can be used to preprocess data using exactly the same code for both training a model and serving inferences in production.\n",
    "\n",
    "TensorFlow Transform is a library for preprocessing input data for TensorFlow, including creating features that require a full pass over the training dataset.  For example, using TensorFlow Transform you could:\n",
    "\n",
    "* Normalize an input value by using the mean and standard deviation\n",
    "* Convert strings to integers by generating a vocabulary over all of the input values\n",
    "* Convert floats to integers by assigning them to buckets, based on the observed data distribution\n",
    "\n",
    "TensorFlow has built-in support for manipulations on a single example or a batch of examples. `tf.Transform` extends these capabilities to support full passes over the entire training dataset.\n",
    "\n",
    "The output of `tf.Transform` is exported as a TensorFlow graph which you can use for both training and serving. Using the same graph for both training and serving can prevent skew, since the same transformations are applied in both stages.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_tQUubddMvnP"
   },
   "source": [
    "## What you're doing in this notebook\n",
    "\n",
    "In this notebook you'll be processing a <a target='_blank' href='https://archive.ics.uci.edu/ml/machine-learning-databases/adult'>widely used dataset containing census data</a>, and training a model to do classification.  Along the way you'll be transforming the data using `tf.Transform`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OeonII4omTr1"
   },
   "source": [
    "### Install TensorFlow Transform\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "9Ak6XDO5mT3m",
    "outputId": "dc6e0fdb-ca03-4882-80b6-e871e95da43f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Building wheels for collected packages: dill\n",
      "  Building wheel for dill (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl size=78544 sha256=09930991cd66588f2c5939dc4c413540a2f0d291f15299d801fa4b83094f7780\n",
      "  Stored in directory: /home/jupyter/.cache/pip/wheels/a4/61/fd/c57e374e580aa78a45ed78d5859b3a44436af17e22ca53284f\n",
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      "Installing collected packages: tensorflow-estimator, libclang, keras, uritemplate, tensorflow-io-gcs-filesystem, pyparsing, protobuf, numpy, dill, pyarrow, httplib2, google-api-core, tensorboard, google-cloud-core, google-api-python-client, tensorflow, google-cloud-vision, google-cloud-videointelligence, google-cloud-spanner, google-cloud-language, google-cloud-datastore, google-cloud-bigtable\n",
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      "  Attempting uninstall: google-cloud-core\n",
      "    Found existing installation: google-cloud-core 2.3.0\n",
      "    Uninstalling google-cloud-core-2.3.0:\n",
      "      Successfully uninstalled google-cloud-core-2.3.0\n",
      "  Attempting uninstall: google-api-python-client\n",
      "    Found existing installation: google-api-python-client 2.49.0\n",
      "    Uninstalling google-api-python-client-2.49.0:\n",
      "      Successfully uninstalled google-api-python-client-2.49.0\n",
      "  Attempting uninstall: tensorflow\n",
      "    Found existing installation: tensorflow 2.6.4\n",
      "    Uninstalling tensorflow-2.6.4:\n",
      "      Successfully uninstalled tensorflow-2.6.4\n",
      "  Attempting uninstall: google-cloud-vision\n",
      "    Found existing installation: google-cloud-vision 2.7.2\n",
      "    Uninstalling google-cloud-vision-2.7.2:\n",
      "      Successfully uninstalled google-cloud-vision-2.7.2\n",
      "  Attempting uninstall: google-cloud-videointelligence\n",
      "    Found existing installation: google-cloud-videointelligence 2.7.0\n",
      "    Uninstalling google-cloud-videointelligence-2.7.0:\n",
      "      Successfully uninstalled google-cloud-videointelligence-2.7.0\n",
      "  Attempting uninstall: google-cloud-spanner\n",
      "    Found existing installation: google-cloud-spanner 3.14.0\n",
      "    Uninstalling google-cloud-spanner-3.14.0:\n",
      "      Successfully uninstalled google-cloud-spanner-3.14.0\n",
      "  Attempting uninstall: google-cloud-language\n",
      "    Found existing installation: google-cloud-language 2.4.2\n",
      "    Uninstalling google-cloud-language-2.4.2:\n",
      "      Successfully uninstalled google-cloud-language-2.4.2\n",
      "  Attempting uninstall: google-cloud-datastore\n",
      "    Found existing installation: google-cloud-datastore 2.6.0\n",
      "    Uninstalling google-cloud-datastore-2.6.0:\n",
      "      Successfully uninstalled google-cloud-datastore-2.6.0\n",
      "  Attempting uninstall: google-cloud-bigtable\n",
      "    Found existing installation: google-cloud-bigtable 2.9.0\n",
      "    Uninstalling google-cloud-bigtable-2.9.0:\n",
      "      Successfully uninstalled google-cloud-bigtable-2.9.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tensorflow-io 0.21.0 requires tensorflow<2.7.0,>=2.6.0, but you have tensorflow 2.8.2 which is incompatible.\n",
      "tensorflow-io 0.21.0 requires tensorflow-io-gcs-filesystem==0.21.0, but you have tensorflow-io-gcs-filesystem 0.26.0 which is incompatible.\n",
      "google-cloud-storage 2.3.0 requires google-cloud-core<3.0dev,>=2.3.0, but you have google-cloud-core 1.7.2 which is incompatible.\n",
      "cloud-tpu-client 0.10 requires google-api-python-client==1.8.0, but you have google-api-python-client 1.12.11 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed dill-0.3.1.1 google-api-core-1.31.6 google-api-python-client-2.48.0 google-cloud-bigtable-1.7.1 google-cloud-core-2.2.2 google-cloud-datastore-1.15.4 google-cloud-language-1.3.1 google-cloud-spanner-1.19.2 google-cloud-videointelligence-1.16.2 google-cloud-vision-1.0.1 httplib2-0.19.1 keras-2.8.0 libclang-14.0.1 numpy-1.21.6 protobuf-3.19.4 pyarrow-5.0.0 pyparsing-2.4.7 tensorboard-2.8.0 tensorflow-2.8.2 tensorflow-estimator-2.8.0 tensorflow-io-gcs-filesystem-0.26.0 uritemplate-3.0.1\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow-transform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_tQUubddMvnP"
   },
   "source": [
    "**Note:** Restart the kernel before proceeding further. Select **Kernel > Restart kernel > Restart** from the menu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R0mXLOJR_-dv",
    "outputId": "4029836b-e8ec-425f-a204-aeac02ec2da7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'pkg_resources' from '/opt/conda/lib/python3.7/site-packages/pkg_resources/__init__.py'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This cell is only necessary because packages were installed while python was\n",
    "# running. It avoids the need to restart the runtime when running in Colab.\n",
    "import pkg_resources\n",
    "import importlib\n",
    "\n",
    "importlib.reload(pkg_resources)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RptgLn2RYuK3"
   },
   "source": [
    "## Imports and globals\n",
    "\n",
    "First import the stuff you need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K4QXVIM7iglN",
    "outputId": "dcd1f9e0-0468-4365-cdbd-5a77e5a6aaa8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF: 2.8.2\n",
      "Beam: 2.39.0\n",
      "Transform: 1.8.0\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import os\n",
    "import pprint\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "print('TF: {}'.format(tf.__version__))\n",
    "\n",
    "import apache_beam as beam\n",
    "print('Beam: {}'.format(beam.__version__))\n",
    "\n",
    "import tensorflow_transform as tft\n",
    "import tensorflow_transform.beam as tft_beam\n",
    "print('Transform: {}'.format(tft.__version__))\n",
    "\n",
    "from tfx_bsl.public import tfxio\n",
    "from tfx_bsl.coders.example_coder import RecordBatchToExamples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sutRmRNSGT5p"
   },
   "source": [
    "Next download the data files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mKEYRl2g_vzl",
    "outputId": "08dfbb0c-8211-4b63-8655-9c0c95b074ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-05-30 14:05:34--  https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.data\n",
      "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.197.128, 74.125.142.128, 74.125.195.128, ...\n",
      "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.197.128|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3974305 (3.8M) [application/octet-stream]\n",
      "Saving to: ‘adult.data’\n",
      "\n",
      "adult.data          100%[===================>]   3.79M  --.-KB/s    in 0.02s   \n",
      "\n",
      "2022-05-30 14:05:35 (187 MB/s) - ‘adult.data’ saved [3974305/3974305]\n",
      "\n",
      "--2022-05-30 14:05:35--  https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.test\n",
      "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.20.128, 108.177.98.128, 74.125.197.128, ...\n",
      "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.20.128|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2003153 (1.9M) [application/octet-stream]\n",
      "Saving to: ‘adult.test’\n",
      "\n",
      "adult.test          100%[===================>]   1.91M  --.-KB/s    in 0.01s   \n",
      "\n",
      "2022-05-30 14:05:35 (165 MB/s) - ‘adult.test’ saved [2003153/2003153]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.data\n",
    "!wget https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.test\n",
    "\n",
    "train_path = './adult.data'\n",
    "test_path = './adult.test'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CxOxaaOYRfl7"
   },
   "source": [
    "### Name our columns\n",
    "You'll create some handy lists for referencing the columns in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "-bsr1nLHqyg_"
   },
   "outputs": [],
   "source": [
    "CATEGORICAL_FEATURE_KEYS = [\n",
    "    'workclass',\n",
    "    'education',\n",
    "    'marital-status',\n",
    "    'occupation',\n",
    "    'relationship',\n",
    "    'race',\n",
    "    'sex',\n",
    "    'native-country',\n",
    "]\n",
    "\n",
    "NUMERIC_FEATURE_KEYS = [\n",
    "    'age',\n",
    "    'capital-gain',\n",
    "    'capital-loss',\n",
    "    'hours-per-week',\n",
    "    'education-num'\n",
    "]\n",
    "\n",
    "ORDERED_CSV_COLUMNS = [\n",
    "    'age', 'workclass', 'fnlwgt', 'education', 'education-num',\n",
    "    'marital-status', 'occupation', 'relationship', 'race', 'sex',\n",
    "    'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'label'\n",
    "]\n",
    "\n",
    "LABEL_KEY = 'label'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R52dXlw0G0CN"
   },
   "source": [
    "Here's a quick preview of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "id": "312cQ5vwGjOu",
    "outputId": "ea978674-b96a-4621-88df-e680268da9b4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country   label  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_train = pd.read_csv(train_path, header=None, names=ORDERED_CSV_COLUMNS)\n",
    "\n",
    "pandas_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "zzjzjR3351j0"
   },
   "outputs": [],
   "source": [
    "one_row = dict(pandas_train.loc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "zk2b8IPd4uPr"
   },
   "outputs": [],
   "source": [
    "COLUMN_DEFAULTS = [\n",
    "  '' if isinstance(v, str) else 0.0\n",
    "  for v in  dict(pandas_train.loc[1]).values()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LefAguV5ICMc"
   },
   "source": [
    "The test data has 1 header line that needs to be skipped, and a trailing \".\" at the end of each line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "id": "RasgDIUKHCpV",
    "outputId": "3fcf00c1-235e-46c8-8205-7f68734125b3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>198693</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass  fnlwgt      education  education-num       marital-status  \\\n",
       "0   38     Private   89814        HS-grad              9   Married-civ-spouse   \n",
       "1   28   Local-gov  336951     Assoc-acdm             12   Married-civ-spouse   \n",
       "2   44     Private  160323   Some-college             10   Married-civ-spouse   \n",
       "3   18           ?  103497   Some-college             10        Never-married   \n",
       "4   34     Private  198693           10th              6        Never-married   \n",
       "\n",
       "           occupation    relationship    race      sex  capital-gain  \\\n",
       "0     Farming-fishing         Husband   White     Male             0   \n",
       "1     Protective-serv         Husband   White     Male             0   \n",
       "2   Machine-op-inspct         Husband   Black     Male          7688   \n",
       "3                   ?       Own-child   White   Female             0   \n",
       "4       Other-service   Not-in-family   White     Male             0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country    label  \n",
       "0             0              50   United-States   <=50K.  \n",
       "1             0              40   United-States    >50K.  \n",
       "2             0              40   United-States    >50K.  \n",
       "3             0              30   United-States   <=50K.  \n",
       "4             0              30   United-States   <=50K.  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_test = pd.read_csv(test_path, header=1, names=ORDERED_CSV_COLUMNS)\n",
    "\n",
    "pandas_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "s9aH5ZnDdD_z"
   },
   "outputs": [],
   "source": [
    "testing = os.getenv(\"WEB_TEST_BROWSER\", False)\n",
    "if testing:\n",
    "  pandas_train = pandas_train.loc[:1]\n",
    "  pandas_test = pandas_test.loc[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qtTn4at8rurk"
   },
   "source": [
    "###Define our features and schema\n",
    "Let's define a schema based on what types the columns are in our input.  Among other things this will help with importing them correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "5oS2RfyCrzMr"
   },
   "outputs": [],
   "source": [
    "RAW_DATA_FEATURE_SPEC = dict(\n",
    "    [(name, tf.io.FixedLenFeature([], tf.string))\n",
    "     for name in CATEGORICAL_FEATURE_KEYS] +\n",
    "    [(name, tf.io.FixedLenFeature([], tf.float32))\n",
    "     for name in NUMERIC_FEATURE_KEYS] + \n",
    "    [(LABEL_KEY, tf.io.FixedLenFeature([], tf.string))]\n",
    ")\n",
    "\n",
    "SCHEMA = tft.tf_metadata.dataset_metadata.DatasetMetadata(\n",
    "    tft.tf_metadata.schema_utils.schema_from_feature_spec(RAW_DATA_FEATURE_SPEC)).schema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_j6M7ObpaLHi"
   },
   "source": [
    "### [Optional] Encode and decode tf.train.Example protos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rgGO9-GkZ5Kv"
   },
   "source": [
    "This tutorial needs to convert examples from the dataset to and from `tf.train.Example` protos in a few places. \n",
    "\n",
    "The hidden `encode_example` function below converts a dictionary of features forom the dataset to a `tf.train.Example`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Wbhndy7uWqYp"
   },
   "outputs": [],
   "source": [
    "#@title\n",
    "def encode_example(input_features):\n",
    "  input_features = dict(input_features)\n",
    "  output_features = {}\n",
    "  \n",
    "  for key in CATEGORICAL_FEATURE_KEYS:\n",
    "    value = input_features[key]\n",
    "    feature = tf.train.Feature(\n",
    "        bytes_list=tf.train.BytesList(value=[value.strip().encode()]))\n",
    "    output_features[key] = feature \n",
    "\n",
    "  for key in NUMERIC_FEATURE_KEYS:\n",
    "    value = input_features[key]\n",
    "    feature = tf.train.Feature(\n",
    "        float_list=tf.train.FloatList(value=[value]))\n",
    "    output_features[key] = feature \n",
    "\n",
    "  label_value = input_features.get(LABEL_KEY, None)\n",
    "  if label_value is not None:\n",
    "    output_features[LABEL_KEY]  = tf.train.Feature(\n",
    "        bytes_list = tf.train.BytesList(value=[label_value.strip().encode()]))\n",
    "\n",
    "  example = tf.train.Example(\n",
    "      features = tf.train.Features(feature=output_features)\n",
    "  )\n",
    "  return example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4qx7fSVmmwIQ"
   },
   "source": [
    "Now you can convert dataset examples into `Example` protos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sWd95yxJceXy",
    "outputId": "ca9089ca-45c3-462e-c54b-831b753fc578"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float_list {\n",
       "  value: 39.0\n",
       "}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_example = encode_example(pandas_train.loc[0])\n",
    "tf_example.features.feature['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EutF2aPXbAUd",
    "outputId": "043601b6-60ba-4e22-f144-9aed3f8a5ad4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-30 14:06:15.528156: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64\n",
      "2022-05-30 14:06:15.528200: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-05-30 14:06:15.528226: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tensorflow-2-6-20220530-191935): /proc/driver/nvidia/version does not exist\n",
      "2022-05-30 14:06:15.528739: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=string, numpy=\n",
       "array([b'\\n\\xf9\\x02\\n\\x12\\n\\x05label\\x12\\t\\n\\x07\\n\\x05<=50K\\n\\x1a\\n\\tworkclass\\x12\\r\\n\\x0b\\n\\tState-gov\\n#\\n\\x0emarital-status\\x12\\x11\\n\\x0f\\n\\rNever-married\\n!\\n\\x0crelationship\\x12\\x11\\n\\x0f\\n\\rNot-in-family\\n\\x0f\\n\\x03age\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00\\x1cB\\n\\x18\\n\\x0ccapital-gain\\x12\\x08\\x12\\x06\\n\\x04\\x00\\xe0\\x07E\\n\\x11\\n\\x04race\\x12\\t\\n\\x07\\n\\x05White\\n\\x19\\n\\reducation-num\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00PA\\n\\x1a\\n\\x0ehours-per-week\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00 B\\n#\\n\\x0enative-country\\x12\\x11\\n\\x0f\\n\\rUnited-States\\n\\x1e\\n\\noccupation\\x12\\x10\\n\\x0e\\n\\x0cAdm-clerical\\n\\x18\\n\\x0ccapital-loss\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00\\x00\\x00\\n\\x1a\\n\\teducation\\x12\\r\\n\\x0b\\n\\tBachelors\\n\\x0f\\n\\x03sex\\x12\\x08\\n\\x06\\n\\x04Male',\n",
       "       b'\\n\\x82\\x03\\n#\\n\\x0enative-country\\x12\\x11\\n\\x0f\\n\\rUnited-States\\n\\x1a\\n\\teducation\\x12\\r\\n\\x0b\\n\\tBachelors\\n\\x1a\\n\\x0ehours-per-week\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00PA\\n\\x0f\\n\\x03age\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00HB\\n\\x18\\n\\x0ccapital-loss\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00\\x00\\x00\\n\\x1b\\n\\x0crelationship\\x12\\x0b\\n\\t\\n\\x07Husband\\n(\\n\\x0emarital-status\\x12\\x16\\n\\x14\\n\\x12Married-civ-spouse\\n!\\n\\tworkclass\\x12\\x14\\n\\x12\\n\\x10Self-emp-not-inc\\n!\\n\\noccupation\\x12\\x13\\n\\x11\\n\\x0fExec-managerial\\n\\x12\\n\\x05label\\x12\\t\\n\\x07\\n\\x05<=50K\\n\\x18\\n\\x0ccapital-gain\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00\\x00\\x00\\n\\x11\\n\\x04race\\x12\\t\\n\\x07\\n\\x05White\\n\\x0f\\n\\x03sex\\x12\\x08\\n\\x06\\n\\x04Male\\n\\x19\\n\\reducation-num\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00PA',\n",
       "       b'\\n\\xf5\\x02\\n\\x12\\n\\x05label\\x12\\t\\n\\x07\\n\\x05<=50K\\n\\x0f\\n\\x03age\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00\\x18B\\n\\x18\\n\\x0ccapital-loss\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00\\x00\\x00\\n!\\n\\x0crelationship\\x12\\x11\\n\\x0f\\n\\rNot-in-family\\n\\x18\\n\\tworkclass\\x12\\x0b\\n\\t\\n\\x07Private\\n#\\n\\noccupation\\x12\\x15\\n\\x13\\n\\x11Handlers-cleaners\\n\\x18\\n\\teducation\\x12\\x0b\\n\\t\\n\\x07HS-grad\\n\\x19\\n\\reducation-num\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00\\x10A\\n\\x11\\n\\x04race\\x12\\t\\n\\x07\\n\\x05White\\n\\x1a\\n\\x0ehours-per-week\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00 B\\n\\x0f\\n\\x03sex\\x12\\x08\\n\\x06\\n\\x04Male\\n\\x18\\n\\x0ccapital-gain\\x12\\x08\\x12\\x06\\n\\x04\\x00\\x00\\x00\\x00\\n\\x1e\\n\\x0emarital-status\\x12\\x0c\\n\\n\\n\\x08Divorced\\n#\\n\\x0enative-country\\x12\\x11\\n\\x0f\\n\\rUnited-States'],\n",
       "      dtype=object)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serialized_example_batch = tf.constant([\n",
    "  encode_example(pandas_train.loc[i]).SerializeToString()\n",
    "  for i in range(3)\n",
    "])\n",
    "\n",
    "serialized_example_batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DTqlJcI_m6az"
   },
   "source": [
    "You can also convert batches of serialized Example protos back into a dictionary of tensors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "jXlrur1vc4n_"
   },
   "outputs": [],
   "source": [
    "decoded_tensors = tf.io.parse_example(\n",
    "    serialized_example_batch,\n",
    "    features=RAW_DATA_FEATURE_SPEC\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eUAcdCrEdDe3"
   },
   "source": [
    "In some cases the label will not be passed in, so the encode function is written so that the label is optional:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EEt3nPr_o59f",
    "outputId": "038e5306-914e-4af5-9cb8-fd9c007ad1cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_dict = dict(pandas_train.loc[0])\n",
    "features_dict.pop(LABEL_KEY)\n",
    "\n",
    "LABEL_KEY in features_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O0yqvsHtpDdX"
   },
   "source": [
    "When creating an `Example` proto it will simply not contain the label key. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7N5FMXO7dRzM",
    "outputId": "c523bfdc-29c9-4230-a820-12a29e1b3977"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_label_example = encode_example(features_dict)\n",
    "\n",
    "LABEL_KEY in no_label_example.features.feature.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zdXy9lo4t45d"
   },
   "source": [
    "### Setting hyperparameters and basic housekeeping\n",
    "\n",
    "Constants and hyperparameters used for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "8WHyOkC9uL71"
   },
   "outputs": [],
   "source": [
    "NUM_OOV_BUCKETS = 1\n",
    "\n",
    "EPOCH_SPLITS = 10\n",
    "TRAIN_NUM_EPOCHS = 2*EPOCH_SPLITS\n",
    "NUM_TRAIN_INSTANCES = len(pandas_train)\n",
    "NUM_TEST_INSTANCES = len(pandas_test)\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "\n",
    "STEPS_PER_TRAIN_EPOCH = tf.math.ceil(NUM_TRAIN_INSTANCES/BATCH_SIZE/EPOCH_SPLITS)\n",
    "EVALUATION_STEPS = tf.math.ceil(NUM_TEST_INSTANCES/BATCH_SIZE)\n",
    "\n",
    "# Names of temp files\n",
    "TRANSFORMED_TRAIN_DATA_FILEBASE = 'train_transformed'\n",
    "TRANSFORMED_TEST_DATA_FILEBASE = 'test_transformed'\n",
    "EXPORTED_MODEL_DIR = 'exported_model_dir'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "lG2uO-88c6R9"
   },
   "outputs": [],
   "source": [
    "if testing:\n",
    "  TRAIN_NUM_EPOCHS = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0a1ns5KswDb2"
   },
   "source": [
    "## Preprocessing with `tf.Transform`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KKd3mCLNVYmg"
   },
   "source": [
    "### Create a `tf.Transform` preprocessing_fn\n",
    "\n",
    "The _preprocessing function_ is the most important concept of tf.Transform. A preprocessing function is where the transformation of the dataset really happens. It accepts and returns a dictionary of tensors, where a tensor means a [`Tensor`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/Tensor) or [`SparseTensor`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/SparseTensor). There are two main groups of API calls that typically form the heart of a preprocessing function:\n",
    "\n",
    "1. **TensorFlow Ops:** Any function that accepts and returns tensors, which usually means TensorFlow ops. These add TensorFlow operations to the graph that transforms raw data into transformed data one feature vector at a time.  These will run for every example, during both training and serving.\n",
    "2. **Tensorflow Transform Analyzers/Mappers:** Any of the analyzers/mappers provided by tf.Transform. These also accept and return tensors, and typically contain a combination of Tensorflow ops and Beam computation, but unlike TensorFlow ops they only run in the Beam pipeline during analysis requiring a full pass over the entire training dataset. The Beam computation runs only once, (prior to training, during analysis), and typically make a full pass over the entire training dataset. They create `tf.constant` tensors, which are added to your graph. For example, `tft.min` computes the minimum of a tensor over the training dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DZopPfpaH4sB"
   },
   "source": [
    "Here is a `preprocessing_fn` for this dataset. It does several things:\n",
    "\n",
    "1. Using `tft.scale_to_0_1`, it scales the numeric features to the `[0,1]` range.\n",
    "2. Using `tft.compute_and_apply_vocabulary`, it computes a vocabulary for each of the categorical features, and returns the integer IDs for each input as an `tf.int64`. This applies both to string and integer categorical-inputs.\n",
    "3. It applies some manual transformations to the data using standard TensorFlow operations. Here these operations are applied to the label but could transform the features as well. The TensorFlow operations do several things:   \n",
    "  * They build a lookup table for the label (the `tf.init_scope` ensures that the table is only created the first time the function is called).\n",
    "  * They normalize the text of the label.\n",
    "  * They convert the label to a one-hot. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "LDrzuYH0WFc2"
   },
   "outputs": [],
   "source": [
    "def preprocessing_fn(inputs):\n",
    "  \"\"\"Preprocess input columns into transformed columns.\"\"\"\n",
    "  # Since you are modifying some features and leaving others unchanged, you\n",
    "  # start by setting `outputs` to a copy of `inputs.\n",
    "  outputs = inputs.copy()\n",
    "\n",
    "  # Scale numeric columns to have range [0, 1].\n",
    "  for key in NUMERIC_FEATURE_KEYS:\n",
    "    outputs[key] = tft.scale_to_0_1(inputs[key])\n",
    "\n",
    "  # For all categorical columns except the label column, you generate a\n",
    "  # vocabulary but do not modify the feature.  This vocabulary is instead\n",
    "  # used in the trainer, by means of a feature column, to convert the feature\n",
    "  # from a string to an integer id.\n",
    "  for key in CATEGORICAL_FEATURE_KEYS:\n",
    "    outputs[key] = tft.compute_and_apply_vocabulary(\n",
    "        tf.strings.strip(inputs[key]),\n",
    "        num_oov_buckets=NUM_OOV_BUCKETS,\n",
    "        vocab_filename=key)\n",
    "\n",
    "  # For the label column you provide the mapping from string to index.\n",
    "  table_keys = ['>50K', '<=50K']\n",
    "  with tf.init_scope():\n",
    "    initializer = tf.lookup.KeyValueTensorInitializer(\n",
    "        keys=table_keys,\n",
    "        values=tf.cast(tf.range(len(table_keys)), tf.int64),\n",
    "        key_dtype=tf.string,\n",
    "        value_dtype=tf.int64)\n",
    "    table = tf.lookup.StaticHashTable(initializer, default_value=-1)\n",
    "\n",
    "  # Remove trailing periods for test data when the data is read with tf.data.\n",
    "  # label_str  = tf.sparse.to_dense(inputs[LABEL_KEY])\n",
    "  label_str = inputs[LABEL_KEY]\n",
    "  label_str = tf.strings.regex_replace(label_str, r'\\.$', '')\n",
    "  label_str = tf.strings.strip(label_str)\n",
    "  data_labels = table.lookup(label_str)\n",
    "  transformed_label = tf.one_hot(\n",
    "      indices=data_labels, depth=len(table_keys), on_value=1.0, off_value=0.0)\n",
    "  outputs[LABEL_KEY] = tf.reshape(transformed_label, [-1, len(table_keys)])\n",
    "\n",
    "  return outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sA1Eg2JXFzzZ"
   },
   "source": [
    "## Syntax\n",
    "\n",
    "You're almost ready to put everything together and use <a target='_blank' href='https://beam.apache.org/'>Apache Beam</a> to run it.\n",
    "\n",
    "Apache Beam uses a <a target='_blank' href='https://beam.apache.org/documentation/programming-guide/#applying-transforms'>special syntax to define and invoke transforms</a>.  For example, in this line:\n",
    "\n",
    "```\n",
    "result = pass_this | 'name this step' >> to_this_call\n",
    "```\n",
    "\n",
    "The method `to_this_call` is being invoked and passed the object called `pass_this`, and <a target='_blank' href='https://stackoverflow.com/questions/50519662/what-does-the-redirection-mean-in-apache-beam-python'>this operation will be referred to as `name this step` in a stack trace</a>.  The result of the call to `to_this_call` is returned in `result`.  You will often see stages of a pipeline chained together like this:\n",
    "\n",
    "```\n",
    "result = apache_beam.Pipeline() | 'first step' >> do_this_first() | 'second step' >> do_this_last()\n",
    "```\n",
    "\n",
    "and since that started with a new pipeline, you can continue like this:\n",
    "\n",
    "```\n",
    "next_result = result | 'doing more stuff' >> another_function()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rgAGOAdFWRn2"
   },
   "source": [
    "### Transform the data\n",
    "\n",
    "Now you're ready to start transforming our data in an Apache Beam pipeline.\n",
    "\n",
    "1. Read in the data using the `tfxio.CsvTFXIO` CSV reader (to process lines of text in a pipeline use `tfxio.BeamRecordCsvTFXIO` instead).\n",
    "1. Analyse and transform the data using the `preprocessing_fn` defined above.\n",
    "1. Write out the result as a `TFRecord` of `Example` protos, which you will use for training a model later\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "PCeYucVoRRfo"
   },
   "outputs": [],
   "source": [
    "def transform_data(train_data_file, test_data_file, working_dir):\n",
    "  \"\"\"Transform the data and write out as a TFRecord of Example protos.\n",
    "\n",
    "  Read in the data using the CSV reader, and transform it using a\n",
    "  preprocessing pipeline that scales numeric data and converts categorical data\n",
    "  from strings to int64 values indices, by creating a vocabulary for each\n",
    "  category.\n",
    "\n",
    "  Args:\n",
    "    train_data_file: File containing training data\n",
    "    test_data_file: File containing test data\n",
    "    working_dir: Directory to write transformed data and metadata to\n",
    "  \"\"\"\n",
    "\n",
    "  # The \"with\" block will create a pipeline, and run that pipeline at the exit\n",
    "  # of the block.\n",
    "  with beam.Pipeline() as pipeline:\n",
    "    with tft_beam.Context(temp_dir=tempfile.mkdtemp()):\n",
    "      # Create a TFXIO to read the census data with the schema. To do this you\n",
    "      # need to list all columns in order since the schema doesn't specify the\n",
    "      # order of columns in the csv.\n",
    "      # You first read CSV files and use BeamRecordCsvTFXIO whose .BeamSource()\n",
    "      # accepts a PCollection[bytes] because you need to patch the records first\n",
    "      # (see \"FixCommasTrainData\" below). Otherwise, tfxio.CsvTFXIO can be used\n",
    "      # to both read the CSV files and parse them to TFT inputs:\n",
    "      # csv_tfxio = tfxio.CsvTFXIO(...)\n",
    "      # raw_data = (pipeline | 'ToRecordBatches' >> csv_tfxio.BeamSource())\n",
    "      train_csv_tfxio = tfxio.CsvTFXIO(\n",
    "          file_pattern=train_data_file,\n",
    "          telemetry_descriptors=[],\n",
    "          column_names=ORDERED_CSV_COLUMNS,\n",
    "          schema=SCHEMA)\n",
    "\n",
    "      # Read in raw data and convert using CSV TFXIO.\n",
    "      raw_data = (\n",
    "          pipeline |\n",
    "          'ReadTrainCsv' >> train_csv_tfxio.BeamSource())\n",
    "\n",
    "      # Combine data and schema into a dataset tuple.  Note that you already used\n",
    "      # the schema to read the CSV data, but you also need it to interpret\n",
    "      # raw_data.\n",
    "      cfg = train_csv_tfxio.TensorAdapterConfig()\n",
    "      raw_dataset = (raw_data, cfg)\n",
    "\n",
    "      # The TFXIO output format is chosen for improved performance.\n",
    "      transformed_dataset, transform_fn = (\n",
    "          raw_dataset | tft_beam.AnalyzeAndTransformDataset(\n",
    "              preprocessing_fn, output_record_batches=True))\n",
    "\n",
    "      # Transformed metadata is not necessary for encoding.\n",
    "      transformed_data, _ = transformed_dataset\n",
    "\n",
    "      # Extract transformed RecordBatches, encode and write them to the given\n",
    "      # directory.\n",
    "      # TODO(b/223384488): Switch to `RecordBatchToExamplesEncoder`.\n",
    "      _ = (\n",
    "          transformed_data\n",
    "          | 'EncodeTrainData' >>\n",
    "          beam.FlatMapTuple(lambda batch, _: RecordBatchToExamples(batch))\n",
    "          | 'WriteTrainData' >> beam.io.WriteToTFRecord(\n",
    "              os.path.join(working_dir, TRANSFORMED_TRAIN_DATA_FILEBASE)))\n",
    "\n",
    "      # Now apply transform function to test data.  In this case you remove the\n",
    "      # trailing period at the end of each line, and also ignore the header line\n",
    "      # that is present in the test data file.\n",
    "      test_csv_tfxio = tfxio.CsvTFXIO(\n",
    "          file_pattern=test_data_file,\n",
    "          skip_header_lines=1,\n",
    "          telemetry_descriptors=[],\n",
    "          column_names=ORDERED_CSV_COLUMNS,\n",
    "          schema=SCHEMA)\n",
    "      raw_test_data = (\n",
    "          pipeline\n",
    "          | 'ReadTestCsv' >> test_csv_tfxio.BeamSource())\n",
    "\n",
    "      raw_test_dataset = (raw_test_data, test_csv_tfxio.TensorAdapterConfig())\n",
    "\n",
    "      # The TFXIO output format is chosen for improved performance.\n",
    "      transformed_test_dataset = (\n",
    "          (raw_test_dataset, transform_fn)\n",
    "          | tft_beam.TransformDataset(output_record_batches=True))\n",
    "\n",
    "      # Transformed metadata is not necessary for encoding.\n",
    "      transformed_test_data, _ = transformed_test_dataset\n",
    "\n",
    "      # Extract transformed RecordBatches, encode and write them to the given\n",
    "      # directory.\n",
    "      _ = (\n",
    "          transformed_test_data\n",
    "          | 'EncodeTestData' >>\n",
    "          beam.FlatMapTuple(lambda batch, _: RecordBatchToExamples(batch))\n",
    "          | 'WriteTestData' >> beam.io.WriteToTFRecord(\n",
    "              os.path.join(working_dir, TRANSFORMED_TEST_DATA_FILEBASE)))\n",
    "\n",
    "      # Will write a SavedModel and metadata to working_dir, which can then\n",
    "      # be read by the tft.TFTransformOutput class.\n",
    "      _ = (\n",
    "          transform_fn\n",
    "          | 'WriteTransformFn' >> tft_beam.WriteTransformFn(working_dir))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "huaj5EgCVRD9"
   },
   "source": [
    "Run the pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 600
    },
    "id": "pjC7eDWFyA8K",
    "outputId": "a6178835-71ef-4a68-bc19-1ae28f508be7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
       "          var jqueryScript = document.createElement('script');\n",
       "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
       "          jqueryScript.type = 'text/javascript';\n",
       "          jqueryScript.onload = function() {\n",
       "            var datatableScript = document.createElement('script');\n",
       "            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n",
       "            datatableScript.type = 'text/javascript';\n",
       "            datatableScript.onload = function() {\n",
       "              window.interactive_beam_jquery = jQuery.noConflict(true);\n",
       "              window.interactive_beam_jquery(document).ready(function($){\n",
       "                \n",
       "              });\n",
       "            }\n",
       "            document.head.appendChild(datatableScript);\n",
       "          };\n",
       "          document.head.appendChild(jqueryScript);\n",
       "        } else {\n",
       "          window.interactive_beam_jquery(document).ready(function($){\n",
       "            \n",
       "          });\n",
       "        }"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:326: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use ref() instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:326: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use ref() instead.\n",
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n",
      "2022-05-30 14:07:03.971040: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpi_fen3nh/tftransform_tmp/9faa28377b124b27ba0efb8398a7526a/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpi_fen3nh/tftransform_tmp/9faa28377b124b27ba0efb8398a7526a/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_decision_forests is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_decision_forests is not available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_text is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_text is not available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpi_fen3nh/tftransform_tmp/4b8abedd80c94c9c8ea7e72429d414ef/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpi_fen3nh/tftransform_tmp/4b8abedd80c94c9c8ea7e72429d414ef/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_decision_forests is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_decision_forests is not available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_text is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_text is not available.\n",
      "WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_decision_forests is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_decision_forests is not available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_text is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_text is not available.\n"
     ]
    }
   ],
   "source": [
    "# TODO 1\n",
    "import tempfile\n",
    "import pathlib\n",
    "\n",
    "output_dir = os.path.join(tempfile.mkdtemp(), 'keras')\n",
    "\n",
    "# Transform the data\n",
    "transform_data(train_path, test_path, output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iqln2AClsA0z"
   },
   "source": [
    "Wrap up the output directory as a `tft.TFTransformOutput`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "FXd4Mgj6sAGB"
   },
   "outputs": [],
   "source": [
    "tf_transform_output = tft.TFTransformOutput(output_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "59hNe7oY9vqG",
    "outputId": "45d1e097-2e92-44a1-e220-85fcbc24edb8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': FixedLenFeature(shape=[], dtype=tf.float32, default_value=None),\n",
       " 'capital-gain': FixedLenFeature(shape=[], dtype=tf.float32, default_value=None),\n",
       " 'capital-loss': FixedLenFeature(shape=[], dtype=tf.float32, default_value=None),\n",
       " 'education': FixedLenFeature(shape=[], dtype=tf.int64, default_value=None),\n",
       " 'education-num': FixedLenFeature(shape=[], dtype=tf.float32, default_value=None),\n",
       " 'hours-per-week': FixedLenFeature(shape=[], dtype=tf.float32, default_value=None),\n",
       " 'label': FixedLenFeature(shape=[2], dtype=tf.float32, default_value=None),\n",
       " 'marital-status': FixedLenFeature(shape=[], dtype=tf.int64, default_value=None),\n",
       " 'native-country': FixedLenFeature(shape=[], dtype=tf.int64, default_value=None),\n",
       " 'occupation': FixedLenFeature(shape=[], dtype=tf.int64, default_value=None),\n",
       " 'race': FixedLenFeature(shape=[], dtype=tf.int64, default_value=None),\n",
       " 'relationship': FixedLenFeature(shape=[], dtype=tf.int64, default_value=None),\n",
       " 'sex': FixedLenFeature(shape=[], dtype=tf.int64, default_value=None),\n",
       " 'workclass': FixedLenFeature(shape=[], dtype=tf.int64, default_value=None)}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_transform_output.transformed_feature_spec()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oBBlL2EIVVF8"
   },
   "source": [
    "If you look in the directory you'll see it contains three things:\n",
    "\n",
    "1. The `train_transformed` and `test_transformed` data files\n",
    "2. The `transform_fn` directory (a `tf.saved_model`)\n",
    "3. The `transformed_metadata` \n",
    "\n",
    "The followning sections show how to use these artifacts to train a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NG6nrHEP2L65",
    "outputId": "039548ff-6091-44f5-ebba-53132c803659"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 15704\n",
      "-rw-r--r-- 1 jupyter jupyter  5356449 May 30 14:07 test_transformed-00000-of-00001\n",
      "-rw-r--r-- 1 jupyter jupyter 10712569 May 30 14:07 train_transformed-00000-of-00001\n",
      "drwxr-xr-x 4 jupyter jupyter     4096 May 30 14:07 transform_fn\n",
      "drwxr-xr-x 2 jupyter jupyter     4096 May 30 14:07 transformed_metadata\n"
     ]
    }
   ],
   "source": [
    "!ls -l {output_dir}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TnaMyRMJ03bR"
   },
   "source": [
    "##Using our preprocessed data to train a model using tf.keras\n",
    "\n",
    "To show how `tf.Transform` enables us to use the same code for both training and serving, and thus prevent skew, you're going to train a model.  To train our model and prepare our trained model for production you need to create input functions.  The main difference between our training input function and our serving input function is that training data contains the labels, and production data does not.  The arguments and returns are also somewhat different."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M8xCZKNc2wAS"
   },
   "source": [
    "### Create an input function for training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "StezlX-Uv0ae"
   },
   "source": [
    "Running the pipeline in the previous section created `TFRecord` files containing the the transformed data.\n",
    "\n",
    "The following code uses `tf.data.experimental.make_batched_features_dataset` and `tft.TFTransformOutput.transformed_feature_spec` to read these data files as a `tf.data.Dataset`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "775Y7BTpHBmb"
   },
   "outputs": [],
   "source": [
    "def _make_training_input_fn(tf_transform_output, train_file_pattern,\n",
    "                            batch_size):\n",
    "  \"\"\"An input function reading from transformed data, converting to model input.\n",
    "\n",
    "  Args:\n",
    "    tf_transform_output: Wrapper around output of tf.Transform.\n",
    "    transformed_examples: Base filename of examples.\n",
    "    batch_size: Batch size.\n",
    "\n",
    "  Returns:\n",
    "    The input data for training or eval, in the form of k.\n",
    "  \"\"\"\n",
    "  def input_fn():\n",
    "    return tf.data.experimental.make_batched_features_dataset(\n",
    "        file_pattern=train_file_pattern,\n",
    "        batch_size=batch_size,\n",
    "        features=tf_transform_output.transformed_feature_spec(),\n",
    "        reader=tf.data.TFRecordDataset,\n",
    "        label_key=LABEL_KEY,\n",
    "        shuffle=True)\n",
    "\n",
    "  return input_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "-b8BgvBvkCnX"
   },
   "outputs": [],
   "source": [
    "# TODO 2\n",
    "train_file_pattern = pathlib.Path(output_dir)/f'{TRANSFORMED_TRAIN_DATA_FILEBASE}*'\n",
    "\n",
    "# Create the input function\n",
    "input_fn = _make_training_input_fn(\n",
    "    tf_transform_output=tf_transform_output,\n",
    "    train_file_pattern = str(train_file_pattern),\n",
    "    batch_size = 10\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q0PwPLBqxsg2"
   },
   "source": [
    "Below you can see a transformed sample of the data. Note how the numeric columns like `education-num` and `hourd-per-week` are converted to floats with a range of [0,1], and the string columns have been converted to IDs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "SpiS26IWlD-1",
    "outputId": "59001a69-6dd4-4d5e-be5c-787fc4ab9292"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>native-country</th>\n",
       "      <th>occupation</th>\n",
       "      <th>race</th>\n",
       "      <th>relationship</th>\n",
       "      <th>sex</th>\n",
       "      <th>workclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.287671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.287671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.232877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.684932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.082192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.346939</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.397260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.518366</td>\n",
       "      <td>6</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.377551</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.479452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.493151</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.436639</td>\n",
       "      <td>2</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.551020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.301370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.479592</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.232877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.244898</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  capital-gain  capital-loss  education  education-num  \\\n",
       "0  0.287671           0.0      0.000000          4       0.666667   \n",
       "1  0.287671           0.0      0.000000          1       0.600000   \n",
       "2  0.232877           0.0      0.000000          1       0.600000   \n",
       "3  0.684932           0.0      0.000000          2       0.800000   \n",
       "4  0.082192           0.0      0.000000          1       0.600000   \n",
       "5  0.397260           0.0      0.518366          6       0.733333   \n",
       "6  0.479452           0.0      0.000000          4       0.666667   \n",
       "7  0.493151           0.0      0.436639          2       0.800000   \n",
       "8  0.301370           0.0      0.000000          0       0.533333   \n",
       "9  0.232877           0.0      0.000000          0       0.533333   \n",
       "\n",
       "   hours-per-week  marital-status  native-country  occupation  race  \\\n",
       "0        0.357143               0               3           0     2   \n",
       "1        0.397959               0               0           2     0   \n",
       "2        0.500000               2               0           3     0   \n",
       "3        0.071429               0               0           2     0   \n",
       "4        0.346939               1              16           5     2   \n",
       "5        0.377551               2               0           3     0   \n",
       "6        0.397959               0               0           1     0   \n",
       "7        0.551020               0               0           2     0   \n",
       "8        0.479592               1               0           6     0   \n",
       "9        0.244898               2               0           0     0   \n",
       "\n",
       "   relationship  sex  workclass  \n",
       "0             0    0          0  \n",
       "1             0    0          4  \n",
       "2             3    1          1  \n",
       "3             0    0          1  \n",
       "4             1    1          0  \n",
       "5             3    0          4  \n",
       "6             0    0          0  \n",
       "7             0    0          0  \n",
       "8             3    0          0  \n",
       "9             3    1          0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for example, label in input_fn().take(1):\n",
    "  break\n",
    "\n",
    "pd.DataFrame(example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yaMzMnij88_v",
    "outputId": "60a3f300-05c0-4391-9134-06d4cca0279d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 2), dtype=float32, numpy=\n",
       "array([[1., 0.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [0., 1.]], dtype=float32)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LyNTX7CO8AAz"
   },
   "source": [
    "### Train, Evaluate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hdg9jXuLWuyK"
   },
   "source": [
    "Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "uK4brUuDTAJ4"
   },
   "outputs": [],
   "source": [
    "def build_keras_model(working_dir):\n",
    "  inputs = build_keras_inputs(working_dir)\n",
    "\n",
    "  encoded_inputs = encode_inputs(inputs)\n",
    "\n",
    "  stacked_inputs = tf.concat(tf.nest.flatten(encoded_inputs), axis=1)\n",
    "  output = tf.keras.layers.Dense(100, activation='relu')(stacked_inputs)\n",
    "  output = tf.keras.layers.Dense(50, activation='relu')(output)\n",
    "  output = tf.keras.layers.Dense(2)(output)\n",
    "  model = tf.keras.Model(inputs=inputs, outputs=output)\n",
    "\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "6fJwIbdCRFER"
   },
   "outputs": [],
   "source": [
    "def build_keras_inputs(working_dir):\n",
    "  tf_transform_output = tft.TFTransformOutput(working_dir)\n",
    "\n",
    "  feature_spec = tf_transform_output.transformed_feature_spec().copy()\n",
    "  feature_spec.pop(LABEL_KEY)\n",
    "\n",
    "  # Build the `keras.Input` objects.\n",
    "  inputs = {}\n",
    "  for key, spec in feature_spec.items():\n",
    "    if isinstance(spec, tf.io.VarLenFeature):\n",
    "      inputs[key] = tf.keras.layers.Input(\n",
    "          shape=[None], name=key, dtype=spec.dtype, sparse=True)\n",
    "    elif isinstance(spec, tf.io.FixedLenFeature):\n",
    "      inputs[key] = tf.keras.layers.Input(\n",
    "          shape=spec.shape, name=key, dtype=spec.dtype)\n",
    "    else:\n",
    "      raise ValueError('Spec type is not supported: ', key, spec)\n",
    "\n",
    "  return inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "9dHD5SoqRqOh"
   },
   "outputs": [],
   "source": [
    "def encode_inputs(inputs):\n",
    "  encoded_inputs = {}\n",
    "  for key in inputs:\n",
    "    feature = tf.expand_dims(inputs[key], -1)\n",
    "    if key in CATEGORICAL_FEATURE_KEYS:\n",
    "      num_buckets = tf_transform_output.num_buckets_for_transformed_feature(key)\n",
    "      encoding_layer = (\n",
    "          tf.keras.layers.CategoryEncoding(\n",
    "              num_tokens=num_buckets, output_mode='binary', sparse=False))\n",
    "      encoded_inputs[key] = encoding_layer(feature)\n",
    "    else:\n",
    "      encoded_inputs[key] = feature\n",
    "  \n",
    "  return encoded_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 776
    },
    "id": "5xNhSq8lTTx3",
    "outputId": "4dd5698b-23bc-455f-b05e-248f1086f7a0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = build_keras_model(output_dir)\n",
    "\n",
    "tf.keras.utils.plot_model(model,rankdir='LR', show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kQSpw_XzXVn1"
   },
   "source": [
    "Build the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "afi3NOC0OMUa"
   },
   "outputs": [],
   "source": [
    "def get_dataset(working_dir, filebase):\n",
    "  tf_transform_output = tft.TFTransformOutput(working_dir)\n",
    "\n",
    "  data_path_pattern = os.path.join(\n",
    "      working_dir,\n",
    "      filebase + '*')\n",
    "  \n",
    "  input_fn = _make_training_input_fn(\n",
    "      tf_transform_output,\n",
    "      data_path_pattern,\n",
    "      batch_size=BATCH_SIZE)\n",
    "  \n",
    "  dataset = input_fn()\n",
    "\n",
    "  return dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-fE_3jyzX_h2"
   },
   "source": [
    "Train and evaluate the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "6i_lhWH8IZrk"
   },
   "outputs": [],
   "source": [
    "# TODO 3\n",
    "def train_and_evaluate(\n",
    "    model,\n",
    "    working_dir):\n",
    "  \"\"\"Train the model on training data and evaluate on test data.\n",
    "\n",
    "  Args:\n",
    "    working_dir: The location of the Transform output.\n",
    "    num_train_instances: Number of instances in train set\n",
    "    num_test_instances: Number of instances in test set\n",
    "\n",
    "  Returns:\n",
    "    The results from the estimator's 'evaluate' method\n",
    "  \"\"\"\n",
    "  train_dataset = get_dataset(working_dir, TRANSFORMED_TRAIN_DATA_FILEBASE)\n",
    "  validation_dataset = get_dataset(working_dir, TRANSFORMED_TEST_DATA_FILEBASE)\n",
    "\n",
    "  model = build_keras_model(working_dir)\n",
    "\n",
    "# Train the model\n",
    "  history = train_model(model, train_dataset, validation_dataset)\n",
    "\n",
    "  metric_values = model.evaluate(validation_dataset,\n",
    "                                 steps=EVALUATION_STEPS,\n",
    "                                 return_dict=True)\n",
    "  return model, history, metric_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "rcVsByIsViRy"
   },
   "outputs": [],
   "source": [
    "def train_model(model, train_dataset, validation_dataset):\n",
    "  model.compile(optimizer='adam',\n",
    "                loss=tf.losses.CategoricalCrossentropy(from_logits=True),\n",
    "                metrics=['accuracy'])\n",
    "\n",
    "  history = model.fit(train_dataset, validation_data=validation_dataset,\n",
    "      epochs=TRAIN_NUM_EPOCHS,\n",
    "      steps_per_epoch=STEPS_PER_TRAIN_EPOCH,\n",
    "      validation_steps=EVALUATION_STEPS)\n",
    "  return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f5xoioogYTle",
    "outputId": "fc69764a-176e-45bd-b6bc-7c73c20e9f39"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "26/26 [==============================] - 2s 32ms/step - loss: 0.4924 - accuracy: 0.7557 - val_loss: 0.4136 - val_accuracy: 0.8096\n",
      "Epoch 2/20\n",
      "26/26 [==============================] - 0s 20ms/step - loss: 0.3950 - accuracy: 0.8233 - val_loss: 0.3706 - val_accuracy: 0.8279\n",
      "Epoch 3/20\n",
      "26/26 [==============================] - 0s 19ms/step - loss: 0.3751 - accuracy: 0.8266 - val_loss: 0.3587 - val_accuracy: 0.8364\n",
      "Epoch 4/20\n",
      "26/26 [==============================] - 0s 20ms/step - loss: 0.3549 - accuracy: 0.8377 - val_loss: 0.3530 - val_accuracy: 0.8343\n",
      "Epoch 5/20\n",
      "26/26 [==============================] - 0s 19ms/step - loss: 0.3414 - accuracy: 0.8434 - val_loss: 0.3521 - val_accuracy: 0.8343\n",
      "Epoch 6/20\n",
      "26/26 [==============================] - 0s 19ms/step - loss: 0.3613 - accuracy: 0.8308 - val_loss: 0.3494 - val_accuracy: 0.8341\n",
      "Epoch 7/20\n",
      "26/26 [==============================] - 1s 20ms/step - loss: 0.3583 - accuracy: 0.8338 - val_loss: 0.3487 - val_accuracy: 0.8344\n",
      "Epoch 8/20\n",
      "26/26 [==============================] - 1s 21ms/step - loss: 0.3371 - accuracy: 0.8428 - val_loss: 0.3549 - val_accuracy: 0.8370\n",
      "Epoch 9/20\n",
      "26/26 [==============================] - 0s 20ms/step - loss: 0.3394 - accuracy: 0.8474 - val_loss: 0.3415 - val_accuracy: 0.8413\n",
      "Epoch 10/20\n",
      "26/26 [==============================] - 1s 29ms/step - loss: 0.3534 - accuracy: 0.8299 - val_loss: 0.3394 - val_accuracy: 0.8421\n",
      "Epoch 11/20\n",
      "26/26 [==============================] - 1s 21ms/step - loss: 0.3381 - accuracy: 0.8359 - val_loss: 0.3387 - val_accuracy: 0.8417\n",
      "Epoch 12/20\n",
      "26/26 [==============================] - 0s 19ms/step - loss: 0.3300 - accuracy: 0.8489 - val_loss: 0.3372 - val_accuracy: 0.8429\n",
      "Epoch 13/20\n",
      "26/26 [==============================] - 0s 18ms/step - loss: 0.3310 - accuracy: 0.8501 - val_loss: 0.3383 - val_accuracy: 0.8431\n",
      "Epoch 14/20\n",
      "26/26 [==============================] - 0s 19ms/step - loss: 0.3348 - accuracy: 0.8425 - val_loss: 0.3345 - val_accuracy: 0.8430\n",
      "Epoch 15/20\n",
      "26/26 [==============================] - 0s 19ms/step - loss: 0.3265 - accuracy: 0.8450 - val_loss: 0.3329 - val_accuracy: 0.8422\n",
      "Epoch 16/20\n",
      "26/26 [==============================] - 1s 29ms/step - loss: 0.3389 - accuracy: 0.8425 - val_loss: 0.3326 - val_accuracy: 0.8441\n",
      "Epoch 17/20\n",
      "26/26 [==============================] - 0s 19ms/step - loss: 0.3407 - accuracy: 0.8450 - val_loss: 0.3329 - val_accuracy: 0.8439\n",
      "Epoch 18/20\n",
      "26/26 [==============================] - 0s 19ms/step - loss: 0.3447 - accuracy: 0.8386 - val_loss: 0.3307 - val_accuracy: 0.8459\n",
      "Epoch 19/20\n",
      "26/26 [==============================] - 0s 18ms/step - loss: 0.3176 - accuracy: 0.8525 - val_loss: 0.3418 - val_accuracy: 0.8410\n",
      "Epoch 20/20\n",
      "26/26 [==============================] - 0s 18ms/step - loss: 0.3222 - accuracy: 0.8468 - val_loss: 0.3304 - val_accuracy: 0.8466\n",
      "128/128 [==============================] - 0s 2ms/step - loss: 0.3307 - accuracy: 0.8467\n"
     ]
    }
   ],
   "source": [
    "model, history, metric_values = train_and_evaluate(model, output_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "id": "gQCbdPIQeXeZ",
    "outputId": "b78ab984-06b2-4dd6-980e-f7f99900ef39"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VJQVZfPXSs7h6ZhmBtMS2FxFJlAK9O7WVsPG3XsDv+iO4iPd9pZM+CRM/CcNnQFr354//tKOGf166iTW7jzB+aD53zZ/AnPHFWIK/GEREuqNAPxEN1d516xsWw3uvQSQEBaUwYYEX8CPPg7TOu1Sccyxdt59/eXETO2samT12EN+8fCJTygb03s/QhTe2VvPIO7vICKQxKDeTorwMBuV6Q1HuB9MFWemk6S8MkT5HgX6ymg7Dlpe8I/dtyyDUDDmDYfw8KJkCRWfC4LO8wO9wBN/xipgrpw7na58Yz8iinJT8KO9W1vKDFzfx5raDDM7LJCs9jUNHW2lsjX8XbCDNGJgTE/J5GQzK8aZLCrO4YsowCrLSe/mnEBEFek9oafBCfeNib9xc+8G6YLZ3QrVoHAwe54V80ZlQdCb1LjOlV8S8X3OUH728hefX7GVgTjq3fWwcfzZ7JJlB76+M5rYwh462cuhoKzVHWzl0tIWahlYON0aXNbQeW3+osZUjjd7zbYpyM7jjsvFcWz5C5wpEepECvac5530pdc1WOLgFDm7zxjVb4fD7HPeFTgWlMHgcjfljWVZdwJM7s6lKL+Wis0cyf+ooZowZggWzEuqjPxHV9S385A9b+c2fdpEeSOMvLhrDX1489pSPqkPhCOv21nHvCxtYvvMwE4cVcPeCSZx3RlH8DRqqoXpTzLAZjuyC0RfB1Otg1IU9/rN3pS0c4d09tUwoyScnI9hr7yvSUxTovamt2bt5qT3gD8YMrfWdbuYsgAUzIZAOgUwIZHjTHZcFM7zp7AGQPQhyBkH2wOh4EI3BQn7zbgP/8c4hjoTSuf7ckfzN3HEM6eHHATvneOHdfXxvySb2HGnkmvGZ3DEDhja/d3yAN9Z8sFFmARRPgLwhsONVaG3wfuFNvgamXu89xz5JNu2v44mKSp5dtYeao63kZwVZNLOMz80exdjivKS9r0hPU6D3Bc5BQ5UX9Id30tLcyOY9B9lYWcPemlrSCVGWH2BCcSZjBmaQaSEIt0KoBcJtEI6OQy0QaoKmWmg65IViJyKBTNKiQX988A/0fjmkpXsneNOCHwyB4PHzaYFou5h55+DQdjiwkciBTbTs20B26IMuKJdZgA2ZCMXjvUtBi8d7YZ0/7INLQVsbYfMSWPsYbPs9uLB3XmLq9XDOIsgfesq7vLaxjcVr9vDEikrWVtaSHjDmThjKpZOG8tqWapau20db2HHRuMH82exRzJ0whGDg9HgahvRfCvQ+bn9tM8+t3sPTK/ewuaqe9IBxyfghfHpGKZdMGHKsvzueSGszv1u1id+8sprm2oPMKnFcMymXkVnNXuA3HvZO7jYdgsZD3rjpsHf1zqnKKvQCe8gE6vLO4Nfv5fCLLVm4vKF8fd4Erp5RltiVMg0HYN3TsPZR2LsKLA3GXuKF+4QrICPxRyuEI443tx3kiYrdvLyhitZQhInDCrhmZhlXTS897tzFgfpmHl++m1//aRf7apsZXpjFZz4ykuvOHUlxfubJ7BGRpFOg9xPOOTbsq+OZlXt4bs1equtbKMxOZ8GUYXx6RhkzRg447pr2N7ZW8/2lm1i/t44JJd517x89K8Hr3p3znhEfCUWHtg7zIW8+3Hb8fCQEOBg4GvKGfujmq1W7DvPd325g1a4jTCkr5O4FkygfPSjxnVC9xTtqX/s41O6C9FzvRq+p18GYj3Z6yejOg0d5YsVunl65h321zQzISeeqaaUsmlnGOaWFXb5lKBxh2cYD/O/b7/PmtoOkB4z55wzj8+eNYuaogbqPoJ9rag2zv66ZfbVNVNU1c7C+lbkTh/TbrjYFej8UCkd4c9tBnlm1h5fW76e5LcLoohyuml7K9JEDeej1Hby57SClA7K547KzWDi1tM9cNx6JOBav2cv3l25if10zV04dzl3zJ1A6IPtEXgR2veWF+/pnoaUW8kpg8iKvz33QGI6SzQvrqniyopJ3dh4izeDis4q5ZuYIPj6p679sOrO9uoFfvfU+T62opL4lxMRhBXxu9iiumj5cJ1H7GOcc9S0h9tc2s6+2mf21TeyvbWF/XVN0vpn9dc3HrsyKlZ0e4LsLz2bRzLIT+4XtnHdiP3+o13WZAgr0fq6hJcSL6/bz9MpK3tpRg3PEvQSxr2lsDfHAazv4z9e2A/DFi8dy65wzTjwY25pp27SUyOpHyXhvGRbTXVTvsmlKyyGQPYD8wkFk5A6ArALvBOyxcaE3zsz/YFn7Sedj5w6On24MG8+uruJ/3n6fTfvryc8McvXMMj533ijO6KdHdn1JKBzhaGuYxtYQR1tCHG0Je+PW9nH85Y2tIeqbQ1Q3tLC/tjnufRSD8zIpKcykpCCbYYVZlBRmUVKQdWw6mJbGnU+t5a0dNVw1bTj/9KnJ5GV28Zlsa4Idr8GWpd59KfX7vO8rHn0BjL/cGwaOSuLeOp4C3Uf2Hmli5a7DXHxWcb+5sWfPkSa+v3QTz6/Ze+xZNyWFWdQ1t1HXFIqO2+LMh44tb26LADCQOuakrWFY+lFmDE1j8mBjSEYL1lwHLXXQcRxuPaXaXVqQiAVpc2k0hdMIESAtmE4wI4f07DyycvJIy8jxuobSs6NDDmTkeOP2+fbpjFwIZnldXKEW72a1bscdl7V4J7fzh3tfgF4w3DvhXDDMG6efwF9CvaQtHOGt7TW8sHYfyzZWUXM08X+XnIwAORlB8jLbx0EG52ccH9jR0B5akEVGsPsT2+GI42evbOPflm1h5KAcfnLDDCaXxXTN1e2DLS96w47XvAsRMvLgjEvgzI97lydvXuJdyQXes5/Gz/fCffj0hJ8DdTIU6NInLN95iO8+v4F399R+aF0wzSjITqcgKxgdp1OQHYyOveWF2d50YXY6s8YMSuxIv60ZWuqjIV/rjVvqvaAPx5w/CLefQ4ieMwiHYqa9cVNzC9urjvB+9RFCLU3k0EK2tTAoI8zA9Dby09rIooVAuAlrbfS2P1mW5t2wFsz0fgHEjgPp3gnu+n3Q1vjhbbMHesF+LOSHQ8EwXF4JdRnF1FgRJUOKycnJS2rwhMIR3trhhfiL6/dzpLGNvMyg1389OI/czAC5mUFvyIhOZwSPW56dHkjqjWvLdx7i9kdWcbChmR9eAAuz12JbXoR9q70GhSO9O8PPmgejL/T2f6ya7bB5qRfuu97yngOVP9zbZvwVMOaiD29zihTo0mdEIo6K9w+TZhwX3NnpgX518vFAfTOrdx1h1e4jrN51hDWVR479+T8wJ51pIwYwozSfGaWZTB6STkGgzbtUs63JC+FARpywjpkOJPDLyjlorsXV7aXx0B4aqnfRXFNJuHYPVr+fjKYqcluqyQ8fJkDkQ5uHSSOUlolLzyGYmUsgKw879hdG+18c0XHsXyEZOV7XlKV5XQ+WdmwIO9hSfZQVu+tYuauW+pYIGcEgU0cOpHxMMeeUDSAjGAScF37ORYeINxAzfWx9zLh9fXr28d1p7V1p6dmJ/5JqbYT3Xqdlwws0rnuBgeEaIhjh4eWkT5wPZ833LrdN9PWO1sDWl71nQW37A7QdhYx8OHOud+Q+7lLvL6tTpEAXSbJwxLGlqp7Vu4+watdhVu06wrbqBtr/e51RnMv0kQOZPnIAU0oHkJWeRksoQms4Qlv7OByhNRShJRShLexoDUVoDYW96XD78ggtbREONrRQVdfMgXpvHK8vOT8zyJCCTIYXpHNm9lFGZ9ZTFjxCsTtE7ZHDHDx8mLq6WoLhZrKthQHBNoZkRSjKaKMgECLbWkhra/R+AbU2evdC9HVpwfjnT2LPq6Rnw+53jutKcWd8jP9LO5e/Wz0Ucov49+unM3tsJ3c/J6Kt2Xu43+Yl3hF8Q5X3y2/U+V64T7jipPvdFegiKVDX3Mba3bVewO8+wurdRzh0An3H8aQHjIxAGkV5mZQUZDGkIJOhBVkMPTb2hiH5meR2daIvKhxxbN5fz8pdh73h/cPsrPG6cYJpxtnDC5g+ciAzRg1kRlkepblgbU2EQm2s2nWIVzbt543NB6hraiEn3bhw7CA+elYR544aQGaAD460I7FH3WHviB6LObrnuCP949fFTIM339rY4ZxJbcx8ffzzKe1jnPdNZWfNh7MuO64rZf3eWr78m1XsrDnKlz82jtvnjjv1Lp9IBPau9MJ90xKo3gjn3QaX3XtSL6dAF+kDnHPsOtTI+r11hCOOjGAaGYE0bxxMIz0QM3/ccju2rDe6pWoaWli168ixkF+zu5amNu8vgKEFmUwaVsDaylpqjraSkxFg7sShXDG5hDnjh5CV3jevuDomEvH+4sjI7bQr5WhLiLufW89TKyuZNWYQ/379NIYV9uCJ5kM7vLuvB4w4qc0V6CJy0kLhCJvaj+LfP8y66I1sV0wexpzxQ3z7lYtPr6zkW8+uIyOYxo8WTeXjk079cRQ9QYEuInISdlQ38OVHVrF+bx1/fsFo7po/IeH7Ppxz1Da1UXm4icrDjdGxN/2JSSVce27PH6Hr1jcRkU6MLc7j6b86n+8t2cQv/m8ny3ce4ic3zGDM4NwuA7t9uqHl+Gcm5WUGKRuYTUso/hfLnCodoYuIJOB3G6r42pNraAtFGDEop8vA9oac48YjBuZQkB085fMgOkIXETlFl04aytK/uYh7X9hIc1uY2WOLPhTehdnpKb2fQoEuIpKgYYXZ/PQzM1JdRqf0NH8REZ9QoIuI+ERCgW5m88xss5ltM7O74qxfaGZrzWy1mVWY2YU9X6qIiHSl2z50MwsA9wOXApXAcjNb7JzbENPs98Bi55wzsynA48CEZBQsIiLxJXKEPgvY5pzb4ZxrBR4FFsY2cM41uA+uf8wFUnMtpIjIaSyRQC8FdsfMV0aXHcfMPmVmm4AXgJt7pjwREUlUIoEe76LKDx2BO+eecc5NAK4C/jHuC5ndEu1jr6iurj6hQkVEpGuJBHolEPvQgTJgb2eNnXOvA2eY2eA46x50zpU758qLi4tPuFgREelcIoG+HBhnZmPMLAO4Hlgc28DMzrTo7VFmNgPIAGp6ulgREelct1e5OOdCZnYb8BIQAB52zq03s1uj6x8ArgY+b2ZtQBNwnUvVQ2JERE5TejiXiEg/0tXDuXSnqIiITyjQRUR8QoEuIuITCnQREZ9QoIuI+IQCXUTEJxToIiI+oUAXEfEJBbqIiE8o0EVEfEKBLiLiEwp0ERGfUKCLiPiEAl1ExCcU6CIiPqFAFxHxCQW6iIhPKNBFRHxCgS4i4hMKdBERn1Cgi4j4hAJdRMQnFOgiIj6hQBcR8QkFuoiITyjQRUR8QoEuIuITCnQREZ9QoIuI+IQCXUTEJxToIiI+oUAXEfEJBbqIiE8o0EVEfEKBLiLiEwkFupnNM7PNZrbNzO6Ks/6zZrY2OvzRzKb2fKkiItKVbgPdzALA/cB8YBJwg5lN6tDsPeCjzrkpwD8CD/Z0oSIi0rVEjtBnAducczucc63Ao8DC2AbOuT865w5HZ98Gynq2TBER6U4igV4K7I6Zr4wu68wXgKXxVpjZLWZWYWYV1dXViVcpIiLdSiTQLc4yF7eh2SV4gX5nvPXOuQedc+XOufLi4uLEqxQRkW4FE2hTCYyImS8D9nZsZGZTgJ8D851zNT1TnoiIJCqRI/TlwDgzG2NmGcD1wOLYBmY2Enga+JxzbkvPlykiIt3p9gjdORcys9uAl4AA8LBzbr2Z3Rpd/wBwN1AE/MzMAELOufLklS0iIh2Zc3G7w5OuvLzcVVRUpOS9RUT6KzNb0dkBs+4UFRHxCQW6iIhPKNBFRHxCgS4i4hMKdBERn1Cgi4j4hAJdRMQnFOgiIj6hQBcR8QkFuoiITyjQRUR8QoEuIuITCnQREZ9QoIuI+IQCXUTEJxToIiI+oUAXEfEJBbqIiE8o0EVEfEKBLiLiEwp0ERGfUKCLiPiEAl1ExCcU6CIiPqFAFxHxCQW6iIhPKNBFRHxCgS4i4hMKdBERn1Cgi4j4hAJdRMQnFOgiIj6hQBcR8QkFuoiITyQU6GY2z8w2m9k2M7srzvoJZvaWmbWY2R09X6aIiHQn2F0DMwsA9wOXApXAcjNb7JzbENPsEHA7cFUyihQRke4lcoQ+C9jmnNvhnGsFHgUWxjZwzh1wzi0H2pJQo4iIJCCRQC8FdsfMV0aXnTAzu8XMKsysorq6+mReQkREOpFIoFucZe5k3sw596Bzrtw5V15cXHwyLyEiIp1IJNArgREx82XA3uSUIyIiJyuRQF8OjDOzMWaWAVwPLE5uWSIicqK6vcrFORcys9uAl4AA8LBzbr2Z3Rpd/4CZlQAVQAEQMbOvAJOcc3XJK11ERGJ1G+gAzrklwJIOyx6Imd6P1xUjIiIpojtFRUR8QoEuIuITCnQREZ9QoIuI+IQCXUTEJxToIiI+oUAXEfEJBbqIiE8o0EVEfEKBLiLiEwp0ERGfUKCLiPiEAl1ExCcU6CIiPqFAFxHxCQW6iIhPKNBFRHxCgS4i4hMKdBERn1Cgi4j4hAJdRMQnFOgiIj6hQBcR8QkFuoiITyjQRUR8QoEuIuITCnQREZ9QoIuI+IQCXUTEJxToIiI+oUAXEfEJBbqIiE8o0EVEfEKBLiLiEwp0ERGfSCjQzWyemW02s21mdlec9WZm90XXrzWzGT1fqoiIdKXbQDezAHA/MB+YBNxgZpM6NJsPjIsOtwD/0cN1iohINxI5Qp8FbHPO7XDOtQKPAgs7tFkI/I/zvA0MMLNhPVyriIh0IZhAm1Jgd8x8JfCRBNqUAvtiG5nZLXhH8AANZrb5hKr9wGDg4Elu2xv6en3Q92tUfadG9Z2avlzfqM5WJBLoFmeZO4k2OOceBB5M4D27LsiswjlXfqqvkyx9vT7o+zWqvlOj+k5NX6+vM4l0uVQCI2Lmy4C9J9FGRESSKJFAXw6MM7MxZpYBXA8s7tBmMfD56NUus4Fa59y+ji8kIiLJ022Xi3MuZGa3AS8BAeBh59x6M7s1uv4BYAlwObANaAT+PHklAz3QbZNkfb0+6Ps1qr5To/pOTV+vLy5z7kNd3SIi0g/pTlEREZ9QoIuI+ESfDvS+/MgBMxthZq+Y2UYzW29mfxOnzRwzqzWz1dHh7t6qL/r+O83s3eh7V8RZn8r9Nz5mv6w2szoz+0qHNr2+/8zsYTM7YGbrYpYNMrPfmdnW6HhgJ9t2+XlNYn0/NLNN0X/DZ8xsQCfbdvl5SGJ995jZnph/x8s72TZV+++xmNp2mtnqTrZN+v47Zc65PjngnYDdDowFMoA1wKQObS4HluJdBz8b+FMv1jcMmBGdzge2xKlvDvDbFO7DncDgLtanbP/F+bfeD4xK9f4DLgZmAOtilv0LcFd0+i7gB538DF1+XpNY3yeAYHT6B/HqS+TzkMT67gHuSOAzkJL912H9vwJ3p2r/nerQl4/Q+/QjB5xz+5xzK6PT9cBGvLtj+5O+8siGucB259z7KXjv4zjnXgcOdVi8EPjv6PR/A1fF2TSRz2tS6nPOveycC0Vn38a7DyQlOtl/iUjZ/mtnZgZcCzzS0+/bW/pyoHf2OIETbZN0ZjYamA78Kc7q88xsjZktNbOze7cyHPCyma2IPnahoz6x//DubejsP1Eq91+7oS56X0V0PCROm76yL2/G+6srnu4+D8l0W7RL6OFOuqz6wv67CKhyzm3tZH0q919C+nKg99gjB5LJzPKAp4CvOOfqOqxeideNMBX4CfBsb9YGXOCcm4H3NMy/NrOLO6zvC/svA/gk8ESc1anefyeiL+zLvwdCwK87adLd5yFZ/gM4A5iG93ynf43TJuX7D7iBro/OU7X/EtaXA73PP3LAzNLxwvzXzrmnO653ztU55xqi00uAdDMb3Fv1Oef2RscHgGfw/qyN1Rce2TAfWOmcq+q4ItX7L0ZVe1dUdHwgTptUfxZvBBYAn3XRDt+OEvg8JIVzrso5F3bORYCHOnnfVO+/IPBp4LHO2qRq/52IvhzoffqRA9H+tv8CNjrn/l8nbUqi7TCzWXj7u6aX6ss1s/z2abwTZ+s6NOsLj2zo9Kgolfuvg8XAjdHpG4Hn4rRJ5POaFGY2D7gT+KRzrrGTNol8HpJVX+x5mU918r4p239RHwc2Oecq461M5f47Iak+K9vVgHcVxha8s99/H112K3BrdNrwvnxjO/AuUN6LtV2I9yfhWmB1dLi8Q323Aevxzti/DZzfi/WNjb7vmmgNfWr/Rd8/By+gC2OWpXT/4f1y2Qe04R01fgEoAn4PbI2OB0XbDgeWdPV57aX6tuH1P7d/Dh/oWF9nn4dequ9X0c/XWryQHtaX9l90+S/bP3cxbXt9/53qoFv/RUR8oi93uYiIyAlQoIuI+IQCXUTEJxToIiI+oUAXEfEJBbqIiE8o0EVEfOL/A+/VnIliGbGxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Train')\n",
    "plt.plot(history.history['val_loss'], label='Eval')\n",
    "plt.ylim(0,max(plt.ylim()))\n",
    "plt.legend()\n",
    "plt.title('Loss');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nYeuthrs27vl"
   },
   "source": [
    "### Transform new data\n",
    "\n",
    "In the previous section the training process used the hard-copies of the transformed data that were generated by `tft_beam.AnalyzeAndTransformDataset` in the `transform_dataset` function. \n",
    "\n",
    "For operating on new data you'll need to load final version of the `preprocessing_fn` that was saved by `tft_beam.WriteTransformFn`. \n",
    "\n",
    "The `TFTransformOutput.transform_features_layer` method loads the `preprocessing_fn` SavedModel from the output directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zxi9aS106CLd"
   },
   "source": [
    "Here's a function to load new, unprocessed batches from a source file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "tMHDZhp82ZjM"
   },
   "outputs": [],
   "source": [
    "def read_csv(file_name, batch_size):\n",
    "  return tf.data.experimental.make_csv_dataset(\n",
    "        file_pattern=file_name,\n",
    "        batch_size=batch_size,\n",
    "        column_names=ORDERED_CSV_COLUMNS,\n",
    "        column_defaults=COLUMN_DEFAULTS,\n",
    "        prefetch_buffer_size=0,\n",
    "        ignore_errors=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 444
    },
    "id": "AradAjmW2vyd",
    "outputId": "8c0e95ae-7b23-4b5f-a400-64bfe85e0d0f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.0</td>\n",
       "      <td>b' Local-gov'</td>\n",
       "      <td>229148.0</td>\n",
       "      <td>b' Some-college'</td>\n",
       "      <td>10.0</td>\n",
       "      <td>b' Married-civ-spouse'</td>\n",
       "      <td>b' Handlers-cleaners'</td>\n",
       "      <td>b' Husband'</td>\n",
       "      <td>b' Black'</td>\n",
       "      <td>b' Male'</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>b' United-States'</td>\n",
       "      <td>b' &lt;=50K.'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.0</td>\n",
       "      <td>b' Self-emp-not-inc'</td>\n",
       "      <td>271486.0</td>\n",
       "      <td>b' HS-grad'</td>\n",
       "      <td>9.0</td>\n",
       "      <td>b' Never-married'</td>\n",
       "      <td>b' Craft-repair'</td>\n",
       "      <td>b' Unmarried'</td>\n",
       "      <td>b' White'</td>\n",
       "      <td>b' Male'</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>b' United-States'</td>\n",
       "      <td>b' &lt;=50K.'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.0</td>\n",
       "      <td>b' Private'</td>\n",
       "      <td>106961.0</td>\n",
       "      <td>b' HS-grad'</td>\n",
       "      <td>9.0</td>\n",
       "      <td>b' Married-civ-spouse'</td>\n",
       "      <td>b' Sales'</td>\n",
       "      <td>b' Husband'</td>\n",
       "      <td>b' White'</td>\n",
       "      <td>b' Male'</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>b' United-States'</td>\n",
       "      <td>b' &lt;=50K.'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.0</td>\n",
       "      <td>b' Self-emp-inc'</td>\n",
       "      <td>201495.0</td>\n",
       "      <td>b' Some-college'</td>\n",
       "      <td>10.0</td>\n",
       "      <td>b' Married-civ-spouse'</td>\n",
       "      <td>b' Exec-managerial'</td>\n",
       "      <td>b' Husband'</td>\n",
       "      <td>b' White'</td>\n",
       "      <td>b' Male'</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>b' United-States'</td>\n",
       "      <td>b' &gt;50K.'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.0</td>\n",
       "      <td>b' Private'</td>\n",
       "      <td>320012.0</td>\n",
       "      <td>b' Some-college'</td>\n",
       "      <td>10.0</td>\n",
       "      <td>b' Married-civ-spouse'</td>\n",
       "      <td>b' Adm-clerical'</td>\n",
       "      <td>b' Wife'</td>\n",
       "      <td>b' White'</td>\n",
       "      <td>b' Female'</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>b' United-States'</td>\n",
       "      <td>b' &lt;=50K.'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age             workclass    fnlwgt         education  education-num  \\\n",
       "0  44.0         b' Local-gov'  229148.0  b' Some-college'           10.0   \n",
       "1  23.0  b' Self-emp-not-inc'  271486.0       b' HS-grad'            9.0   \n",
       "2  35.0           b' Private'  106961.0       b' HS-grad'            9.0   \n",
       "3  42.0      b' Self-emp-inc'  201495.0  b' Some-college'           10.0   \n",
       "4  54.0           b' Private'  320012.0  b' Some-college'           10.0   \n",
       "\n",
       "           marital-status             occupation   relationship       race  \\\n",
       "0  b' Married-civ-spouse'  b' Handlers-cleaners'    b' Husband'  b' Black'   \n",
       "1       b' Never-married'       b' Craft-repair'  b' Unmarried'  b' White'   \n",
       "2  b' Married-civ-spouse'              b' Sales'    b' Husband'  b' White'   \n",
       "3  b' Married-civ-spouse'    b' Exec-managerial'    b' Husband'  b' White'   \n",
       "4  b' Married-civ-spouse'       b' Adm-clerical'       b' Wife'  b' White'   \n",
       "\n",
       "          sex  capital-gain  capital-loss  hours-per-week     native-country  \\\n",
       "0    b' Male'           0.0           0.0            40.0  b' United-States'   \n",
       "1    b' Male'           0.0           0.0            40.0  b' United-States'   \n",
       "2    b' Male'           0.0           0.0            60.0  b' United-States'   \n",
       "3    b' Male'           0.0           0.0            40.0  b' United-States'   \n",
       "4  b' Female'           0.0           0.0            40.0  b' United-States'   \n",
       "\n",
       "        label  \n",
       "0  b' <=50K.'  \n",
       "1  b' <=50K.'  \n",
       "2  b' <=50K.'  \n",
       "3   b' >50K.'  \n",
       "4  b' <=50K.'  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for ex in read_csv(test_path, batch_size=5):\n",
    "  break\n",
    "\n",
    "pd.DataFrame(ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OX1f6SgM6LZc"
   },
   "source": [
    "Load the `tft.TransformFeaturesLayer` to transform this data with the `preprocessing_fn`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "id": "nma2Bzi--11x",
    "outputId": "4cdfbf4c-af41-4f9f-bd50-2fa014c0b0ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:struct2tensor is not available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_decision_forests is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_decision_forests is not available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_text is not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:tensorflow_text is not available.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>relationship</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>education</th>\n",
       "      <th>native-country</th>\n",
       "      <th>education-num</th>\n",
       "      <th>occupation</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>race</th>\n",
       "      <th>workclass</th>\n",
       "      <th>age</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-loss</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.397959</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.533333</td>\n",
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       "      <td>0.397959</td>\n",
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       "      <td>0.082192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>4</td>\n",
       "      <td>0.602041</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.342466</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.506849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   relationship  marital-status  education  native-country  education-num  \\\n",
       "0             0               0          1               0       0.600000   \n",
       "1             3               1          0               0       0.533333   \n",
       "2             0               0          0               0       0.533333   \n",
       "3             0               0          1               0       0.600000   \n",
       "4             4               0          1               0       0.600000   \n",
       "\n",
       "   occupation  hours-per-week  race  workclass       age  capital-gain  sex  \\\n",
       "0           9        0.397959     1          2  0.369863           0.0    0   \n",
       "1           1        0.397959     0          1  0.082192           0.0    0   \n",
       "2           4        0.602041     0          0  0.246575           0.0    0   \n",
       "3           2        0.397959     0          5  0.342466           0.0    0   \n",
       "4           3        0.397959     0          0  0.506849           0.0    1   \n",
       "\n",
       "   capital-loss  \n",
       "0           0.0  \n",
       "1           0.0  \n",
       "2           0.0  \n",
       "3           0.0  \n",
       "4           0.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex2 = ex.copy()\n",
    "ex2.pop('fnlwgt')\n",
    "\n",
    "tft_layer = tf_transform_output.transform_features_layer()\n",
    "t_ex = tft_layer(ex2)\n",
    "\n",
    "label = t_ex.pop(LABEL_KEY)\n",
    "pd.DataFrame(t_ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P43ixyQNz1zq"
   },
   "source": [
    "The `tft_layer` is smart enough to still execute the transformation if only a subset of features are passed in. For example, if you only pass in two features, you'll get just the transformed versions of those features back: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "swEPuZsR0Y5S",
    "outputId": "e8f0ce0b-c50e-4923-b255-76dae4625041"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b' Some-college'</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b' HS-grad'</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b' HS-grad'</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b' Some-college'</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b' Some-college'</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          education  hours-per-week\n",
       "0  b' Some-college'            40.0\n",
       "1       b' HS-grad'            40.0\n",
       "2       b' HS-grad'            60.0\n",
       "3  b' Some-college'            40.0\n",
       "4  b' Some-college'            40.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex2 = pd.DataFrame(ex)[['education', 'hours-per-week']]\n",
    "ex2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "_s4SxutV1DTI",
    "outputId": "958c0cd6-32a1-44f6-9ac3-1a9b73c5f392"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.397959</td>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.397959</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.602041</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.397959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.397959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   education  hours-per-week\n",
       "0          1        0.397959\n",
       "1          0        0.397959\n",
       "2          0        0.602041\n",
       "3          1        0.397959\n",
       "4          1        0.397959"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(tft_layer(dict(ex2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x5wo3dN-vhFL"
   },
   "source": [
    "Here's a more robust version that drops features that are not in the feature-spec, and returns a `(features, label)` pair if the label is in the provided features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "hdMKDnafJh64"
   },
   "outputs": [],
   "source": [
    "class Transform(tf.Module):\n",
    "  def __init__(self, working_dir):\n",
    "    self.working_dir = working_dir\n",
    "    self.tf_transform_output = tft.TFTransformOutput(working_dir)\n",
    "    self.tft_layer = tf_transform_output.transform_features_layer()\n",
    "  \n",
    "  @tf.function\n",
    "  def __call__(self, features):\n",
    "    raw_features = {}\n",
    "\n",
    "    for key, val in features.items():\n",
    "      # Skip unused keys\n",
    "      if key not in RAW_DATA_FEATURE_SPEC:\n",
    "        continue\n",
    "\n",
    "      raw_features[key] = val\n",
    "\n",
    "    # Apply the `preprocessing_fn`.\n",
    "    transformed_features = tft_layer(raw_features)\n",
    "    \n",
    "    if LABEL_KEY in transformed_features:\n",
    "      # Pop the label and return a (features, labels) pair.\n",
    "      data_labels = transformed_features.pop(LABEL_KEY)\n",
    "      return (transformed_features, data_labels)\n",
    "    else:\n",
    "      return transformed_features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "mm5HI578Ku1B"
   },
   "outputs": [],
   "source": [
    "transform = Transform(output_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "4jeenwN_3ZRj"
   },
   "outputs": [],
   "source": [
    "t_ex, t_label = transform(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 267
    },
    "id": "yIavZAqALO8H",
    "outputId": "efb153b4-9de6-4bd0-fa75-7e10021b4d96"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>relationship</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>education</th>\n",
       "      <th>native-country</th>\n",
       "      <th>education-num</th>\n",
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       "      <th>age</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-loss</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.369863</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>1</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.082192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>4</td>\n",
       "      <td>0.602041</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.342466</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.506849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   relationship  marital-status  education  native-country  education-num  \\\n",
       "0             0               0          1               0       0.600000   \n",
       "1             3               1          0               0       0.533333   \n",
       "2             0               0          0               0       0.533333   \n",
       "3             0               0          1               0       0.600000   \n",
       "4             4               0          1               0       0.600000   \n",
       "\n",
       "   occupation  hours-per-week  race  workclass       age  capital-gain  sex  \\\n",
       "0           9        0.397959     1          2  0.369863           0.0    0   \n",
       "1           1        0.397959     0          1  0.082192           0.0    0   \n",
       "2           4        0.602041     0          0  0.246575           0.0    0   \n",
       "3           2        0.397959     0          5  0.342466           0.0    0   \n",
       "4           3        0.397959     0          0  0.506849           0.0    1   \n",
       "\n",
       "   capital-loss  \n",
       "0           0.0  \n",
       "1           0.0  \n",
       "2           0.0  \n",
       "3           0.0  \n",
       "4           0.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(t_ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LVQead0fwVuy"
   },
   "source": [
    "Now you can use `Dataset.map` to apply that transformation, on the fly to new data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VN3IO6u1Mk83",
    "outputId": "56156fd0-7a43-47be-a551-3d7ec000a051"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128/128 [==============================] - 1s 4ms/step - loss: 0.3027 - accuracy: 0.8781\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'loss': 0.30266374349594116, 'accuracy': 0.878125011920929}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO 4\n",
    "# Evaluate the model\n",
    "model.evaluate(\n",
    "    read_csv(test_path, batch_size=5).map(transform),\n",
    "    steps=EVALUATION_STEPS,\n",
    "    return_dict=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ymlco3hfU_-E"
   },
   "source": [
    "### Export the model\n",
    "\n",
    "So you have a trained model, and a method to apply the `preporcessing_fn` to new data. Assemble them into a new model that accepts serialized `tf.train.Example` protos as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "AZ2WICuwEwqC"
   },
   "outputs": [],
   "source": [
    "class ServingModel(tf.Module):\n",
    "  def __init__(self, model, working_dir):\n",
    "    self.model = model\n",
    "    self.working_dir = working_dir\n",
    "    self.transform = Transform(working_dir)\n",
    "\n",
    "  @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string)])\n",
    "  def __call__(self, serialized_tf_examples):\n",
    "    # parse the tf.train.Example\n",
    "    feature_spec = RAW_DATA_FEATURE_SPEC.copy()\n",
    "    feature_spec.pop(LABEL_KEY)\n",
    "    parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)\n",
    "    # Apply the `preprocessing_fn`\n",
    "    transformed_features = self.transform(parsed_features)\n",
    "    # Run the model\n",
    "    outputs = self.model(transformed_features)\n",
    "    # Format the output\n",
    "    classes_names = tf.constant([['0', '1']])\n",
    "    classes = tf.tile(classes_names, [tf.shape(outputs)[0], 1])\n",
    "    return {'classes': classes, 'scores': outputs}\n",
    "\n",
    "  def export(self, output_dir):\n",
    "    # Increment the directory number. This is required in order to make this\n",
    "    # model servable with model_server.\n",
    "    save_model_dir = pathlib.Path(output_dir)/'model'\n",
    "    number_dirs = [int(p.name) for p in save_model_dir.glob('*')\n",
    "                  if p.name.isdigit()]\n",
    "    id = max([0] + number_dirs)+1\n",
    "    save_model_dir = save_model_dir/str(id)\n",
    "\n",
    "    # Set the signature to make it visible for serving.\n",
    "    concrete_serving_fn = self.__call__.get_concrete_function()\n",
    "    signatures = {'serving_default': concrete_serving_fn}\n",
    "\n",
    "    # Export the model.\n",
    "    tf.saved_model.save(\n",
    "        self,\n",
    "        str(save_model_dir),\n",
    "        signatures=signatures)\n",
    "    \n",
    "    return save_model_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M8TZf2di24L2"
   },
   "source": [
    "Build the model and test-run it on the batch of serialized examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u2mSC1UMGAwJ",
    "outputId": "b83b2816-27da-43ca-b224-6b4a27d6fc78"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classes': <tf.Tensor: shape=(3, 2), dtype=string, numpy=\n",
       " array([[b'0', b'1'],\n",
       "        [b'0', b'1'],\n",
       "        [b'0', b'1']], dtype=object)>,\n",
       " 'scores': <tf.Tensor: shape=(3, 2), dtype=float32, numpy=\n",
       " array([[-1.5468851 ,  1.457006  ],\n",
       "        [-0.25523075,  0.02256053],\n",
       "        [-1.9011399 ,  1.7598034 ]], dtype=float32)>}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serving_model = ServingModel(model, output_dir)\n",
    "\n",
    "serving_model(serialized_example_batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BWhighof3AK8"
   },
   "source": [
    "Export the model as a SavedModel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kodDWTJIEr77",
    "outputId": "424dbd3e-f6df-4481-ced9-66ec59614ef0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmps2c64whv/keras/model/1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmps2c64whv/keras/model/1/assets\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('/tmp/tmps2c64whv/keras/model/1')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO 5\n",
    "# Export the model\n",
    "saved_model_dir = serving_model.export(output_dir)\n",
    "saved_model_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ohbWxp3-3aQu"
   },
   "source": [
    "Reload the the model and test it on the same batch of examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "nShh6GqcEr78"
   },
   "outputs": [],
   "source": [
    "reloaded = tf.saved_model.load(str(saved_model_dir))\n",
    "run_model = reloaded.signatures['serving_default']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UiYJhQySEr78",
    "outputId": "ca8a868b-0b4f-4163-9a74-8438552cb0db"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classes': <tf.Tensor: shape=(3, 2), dtype=string, numpy=\n",
       " array([[b'0', b'1'],\n",
       "        [b'0', b'1'],\n",
       "        [b'0', b'1']], dtype=object)>,\n",
       " 'scores': <tf.Tensor: shape=(3, 2), dtype=float32, numpy=\n",
       " array([[-1.5468851 ,  1.457006  ],\n",
       "        [-0.25523075,  0.02256053],\n",
       "        [-1.9011399 ,  1.7598034 ]], dtype=float32)>}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_model(serialized_example_batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ICqetCnSjwp1"
   },
   "source": [
    "## What you did\n",
    "\n",
    "In this notebook, you used `tf.Transform` to preprocess a dataset of census data, and train a model with the cleaned and transformed data. You also created an input function that you could use when you deploy our trained model in a production environment to perform inference. By using the same code for both training and inference you avoid any issues with data skew.  Along the way you learned about creating an Apache Beam transform to perform the transformation that you needed for cleaning the data. You also saw how to use this transformed data to train a model using `tf.keras`.  This is just a small piece of what TensorFlow Transform can do!  You encourage you to dive into `tf.Transform` and discover what it can do for you."
   ]
  }
 ],
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